Introduction to anonymisation techniques for social sciences research data
| Dates: | 22 May 2025 |
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| Times: | 10:00 - 11:30 |
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| What is it: | Workshop |
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| Organiser: | Cathie Marsh Institute for Social Research |
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| How much: | Free |
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Researchers are often at a crossroads on how to share data collected from human participants ethically while complying with the rigorous standards set by UK General Data Protection Regulation and the Data Protection Act. This introductory workshop offers a foundational understanding of data anonymisation principles and practices tailored for social sciences research.
This session is designed to equip researchers with essential knowledge and skills to navigate the complexities of data anonymisation. It will cover practical approaches for safeguarding privacy in the most common data types in social sciences, with a focus on quantitative survey data, and qualitative transcripts; brief considerations for audio/visual materials are included. We will cover key concepts as defined by applicable data protection legislation and outlined by the Information Commissioner's Office (ICO) guidance, from definitions of personal data to identifiability and effective anonymisation.
During this free 90-minute online workshop, participants will learn the nuances of de-identification versus anonymisation techniques, how to differentiate between directly identifying information and indirect identifiers and how to best handle special category data, with the view of sharing data ethically and legally.
This session includes live exercises using Mentimeter and will conclude with a dedicated Q&A session administered via Padlet.
Presenters: Maureen Haaker and Cristina Magder
Level: Introductory
Experience/knowledge required: Basic understanding of research methods in the social sciences and of data protection legislation are recommended but not required
Target audience: Academic and non-academic data professionals such as researchers, data managers, data stewards and others involved in collecting data, or managing datasets for wider sharing and re-use.
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